A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer

dc.creatorFernández Blanco, Juan Carlos
dc.creatorCorrales Barrios, Luis Benigno
dc.creatorHernández González, Félix Herminio
dc.creatorBenitez Pina, Israel Francisco
dc.creatorNúñez Alvarez, José Ricardo
dc.date2021-12-06T22:42:06Z
dc.date2021-12-06T22:42:06Z
dc.date2021
dc.date.accessioned2023-10-03T20:03:00Z
dc.date.available2023-10-03T20:03:00Z
dc.identifier0302-9743
dc.identifier1611-3349
dc.identifierhttps://hdl.handle.net/11323/8946
dc.identifierhttps://doi.org/10.1007/978-3-030-89691-1_19
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174110
dc.descriptionFor the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair and maintenance costs. Recently, several methods have been developed that use artificial intelligence techniques such as neural networks, support vector machines, hybrid techniques, etc., for the diagnosis of faults in power transformers using gas analysis. These methods, although they present very good results, encounter restrictions to determine the precise moment before the occurrence of multiple fault of small magnitude and are difficult to implement in practice. This document proposes a method to diagnose multiple incipient faults in a power transformer using fuzzy logic. The proposal, based on historical data from the composition of the gases dissolved in the oil, achieves a performance in the classification of multiple incipient fault of 98.3%. With reliable samples of dissolved gas, it guarantees an overall rate of accuracy in detecting incipient faults that is superior to that obtained by the most successful conventional methods in the industry. The proposal does not encounter generalization difficulties and constitutes a simple solution that allows determining the state of the transformer in service without affecting the continuity of the electricity supply.
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.relation1. T. Committee. IEEE Guide for the Interpretation of Gases Generated in Mineral Oil-Immersed Transformers, IEEE Std C57.104™ (2019). https://doi.org/10.1109/IEEESTD.2019.8890040
dc.relation2. Alzghoul, A., Backe, B., Löfstrand, M., Byström, A., Liljedahl, B.: Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: a hydraulic drive system application. Comput. Ind. 65(8), 1126–1135 (2014). https://doi.org/10.1016/j.compind.2014.06.003
dc.relation3. Chang, C.-K., Shan, J., Chang, K.-C., Pan, J.-S.: Insulation faults diagnosis of power transformer by decision tree with fuzzy logic. In: Pan, J.-S., Lin, J.-W., Liang, Y., Chu, S.-C. (eds.) ICGEC 2019. AISC, vol. 1107, pp. 310–317. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-3308-2_35
dc.relation4. Guo, C., et al.: Transformer failure diagnosis using fuzzy association rule mining combined with case-based reasoning. IET Gener. Transm. Distrib. 14(11), 2202–2208 (2020). https://doi.org/10.1049/iet-gtd.2019.1423
dc.relation5. Duan, J., He, Y., Wu, X.: Assisted diagnosis of real-virtual twin space for data insufficiency. In: Chen, W., Yang, Q., Wang, L., Liu, D., Han, X., Meng, G. (eds.) The Proceedings of the 9th Frontier Academic Forum of Electrical Engineering. LNEE, vol. 743, pp. 387–395. Springer, Singapore (2021). https://doi.org/10.1007/978-981-33-6609-1_34
dc.relation6. Singh, A.K., Saxena, A., Roy, N., Choudhury, U.: Inter-turn fault stability enrichment and diagnostic analysis of power system network using wavelet transformation-based sample data control and fuzzy logic controller. Trans. Inst. Measur. Control, 01423312211007006 (2021). https://doi.org/10.1177/01423312211007006
dc.relation7. Sahoo, S., Chowdary, K.V.V.S.R., Das, S.: DGA and AI technique for fault diagnosis in distribution transformer. In: Sherpa, K.S., Bhoi, A.K., Kalam, A., Mishra, M.K. (eds.) ETAEERE 2020. LNEE, vol. 691, pp. 35–46. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-7511-2_4
dc.relation8. Tahir, M., Tenbohlen, S.: Transformer winding condition assessment using feedforward artificial neural network and frequency response measurements. Energies 14(11), 3227 (2021). https://doi.org/10.3390/en14113227
dc.relation9. Tao, L., Yang, X., Zhou, Y., Yang, L.: A novel transformers fault diagnosis method based on probabilistic neural network and bio-inspired optimizer. Sensors 21(11), 3623 (2021). https://doi.org/10.3390/s21113623
dc.relation10. Mo, W., Kari, T., Wang, H., Luan, L., Gao, W.: Power transformer fault diagnosis using support vector machine and particle swarm optimization. In: 2017 10th International Symposium on Computational Intelligence and Design (ISCID), 2017, vol. 1, pp. 511–515: IEEE. Hangzhou, China (2017). https://doi.org/10.1109/ISCID.2017.165
dc.relation11. Kazemi, Z., Naseri, F., Yazdi, M., Farjah, E.: An EKF-SVM machine learning-based approach for fault detection and classification in three-phase power transformers. IET Sci. Meas. Technol. 15(2), 130–142 (2021). https://doi.org/10.1049/smt2.12015
dc.relation12. Velásquez, R.M.A.: Support vector machine and tree models for oil and Kraft degradation in power transformers. Eng. Fail. Anal. 127, 105488 (2021). https://doi.org/10.1016/j.engfailanal.2021.105488
dc.relation13. Hoballah, A., Mansour, D.-E.A., Taha, I.B.: Hybrid grey wolf optimizer for transformer fault diagnosis using dissolved gases considering uncertainty in measurements. IEEE Access 8, 139176–139187 (2020). https://doi.org/10.1109/ACCESS.2020.3012633
dc.relation14. Shiling, Z.: Application of joint immune ant colony algorithm and fuzzy neural network to path planning and visual image processing of inspection robot in substation. In: 2020 3rd International Conference on Artificial Intelligence and Big Data (ICAIBD), 2020, pp. 142–148. IEEE, Chengdu, China (2020). https://doi.org/10.1109/ICAIBD49809.2020.9137437
dc.relation15. Taha, I.B., Hoballah, A., Ghoneim, S.S.: Optimal ratio limits of rogers’ four-ratios and IEC 60599 code methods using particle swarm optimization fuzzy-logic approach. IEEE Trans. Dielectr. Electr. Insul. 27(1), 222–230 (2020). https://doi.org/10.1109/TDEI.2019.008395
dc.relation16. Tightiz, L., Nasab, M.A., Yang, H., Addeh, A.: An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis. ISA Trans. 103, 63–74 (2020). https://doi.org/10.1016/j.isatra.2020.03.022
dc.relation17. Malik, H., Sharma, R., Mishra, S.: Fuzzy reinforcement learning based intelligent classifier for power transformer faults. ISA Trans. 101, 390–398 (2020). https://doi.org/10.1016/j.isatra.2020.01.016
dc.relation18. Palke, R., Korde, P.: Dissolved Gas Analysis (DGA) to diagnose the internal faults of power transformer by using fuzzy logic method. In: 2020 International Conference on Communication and Signal Processing (ICCSP), 2020, pp. 1050–1053. IEEE, Chennai, India (2020). https://doi.org/10.1109/ICCSP48568.2020.9182279
dc.relation19. Prasojo, R.A., Gumilang, H., Maulidevi, N.U., Soedjarno, B.A.: A fuzzy logic model for power transformer faults’ severity determination based on gas level, gas rate, and dissolved gas analysis interpretation. Energies 13(4), 1009 (2020). https://doi.org/10.3390/en13041009
dc.relation20. Abdo, A., Liu, H., Zhang, H., Guo, J., Li, Q.: A new model of faults classification in power transformers based on data optimization method. Electric Power Syst. Res. 200, 107446 (2021). https://doi.org/10.1016/j.epsr.2021.107446
dc.relation21. Tenbohlen, S., Jagers, J., Vahidi, F., Standardized survey of transformer reliability: on behalf of CIGRE WG A2. 37. In: 2017 International Symposium on Electrical Insulating Materials (ISEIM), 2017, vol. 2, pp. 593–596. IEEE, Toyohashi, Japan (2017). https://doi.org/10.23919/ISEIM.2017.8166559
dc.relation22. Blanco, J.C.F., González, F.H.H., Barrios, L.B.C.: Método de lógica difusa para el diagnóstico de fallos incipientes en un transformador de 40MVA. Rev. Ing. Electrón. Autom. y Com. 42(2), 76–88 (2021). 1815-5928
dc.relation23. Li, E., Wang, L., Song, B.: Fault diagnosis of power transformers with membership degree. IEEE Access 7, 28791–28798 (2019). https://doi.org/10.1109/ACCESS.2019.2902299
dc.relation24. Mohamad, F., Hosny, K., Barakat, T.: incipient fault detection of electric power transformers using fuzzy logic based on roger’s and IEC method. In: 2019 14th International Conference on Computer Engineering and Systems (ICCES), 2019, pp. 303–309. IEEE, Cairo, Egypt (2019). https://doi.org/10.1109/ICCES48960.2019.9068132
dc.relation25. Niţu, M.-C., Aciu, A.-M., Nicola, C.-I., Nicola, M.: Power transformer fault diagnosis using fuzzy logic technique based on dissolved gas analysis and furan analysis. In: 2017 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM) and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics (ACEMP), 2017, pp. 184–189. IEEE, Brasov, Romania (2017). https://doi.org/10.1109/OPTIM.2017.7974968
dc.relation26. Duval, M., Lamarre, L.: The duval pentagon-a new complementary tool for the interpretation of dissolved gas analysis in transformers. IEEE Electr. Insul. Mag. 30(6), 9–12 (2014). https://doi.org/10.1109/MEI.2014.6943428
dc.relation27. Duval, M., DePabla, A.: Interpretation of gas-in-oil analysis using new IEC publication 60599 and IEC TC 10 databases. IEEE Electr. Insul. Mag. 17(2), 31–41 (2001) %@ 0883–7554. https://doi.org/10.1109/57.917529
dc.relation28. Faiz, J., Soleimani, M.: Dissolved gas analysis evaluation in electric power transformers using conventional methods a review. IEEE Trans. Dielectr. Electr. Insul. 24(2), 1239–1248 (2017). https://doi.org/10.1109/TDEI.2017.005959
dc.relation29. Mahmoudi, N., Samimi, M.H., Mohseni, H.: Experiences with transformer diagnosis by DGA: case studies. IET Gener. Transm. Distrib. 13(23), 5431–5439 (2019). https://doi.org/10.1049/iet-gtd.2019.1056
dc.relation30. Rahman, O., Wani, S.A., Parveen, S., Khan, S.A.: Detection of incipient fault in transformer using DGA based integrated intelligent method. In: 2019 International Conference on Power Electronics, Control and Automation (ICPECA), 2019, pp. 1–6. IEEE, New Delhi, India (2019). https://doi.org/10.1109/ICPECA47973.2019.8975638
dc.relation31. Pattanadech, N., Wattakapaiboon, W.: Application of Duval pentagon compared with Other DGA interpretation techniques: case studies for actual transformer inspections including experience from power plants in Thailand. In: 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST), 2019, pp. 1–4. IEEE, Luang Prabang, Laos (2019). https://doi.org/10.1109/ICEAST.2019.8802523
dc.rightsCC0 1.0 Universal
dc.rightshttp://creativecommons.org/publicdomain/zero/1.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourceLecture Notes in Computer Science
dc.sourcehttps://link.springer.com/chapter/10.1007/978-3-030-89691-1_19
dc.subjectPower transformer
dc.subjectFault diagnosis
dc.subjectFuzzy logic
dc.subjectDissolved gas analysis
dc.titleA fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer
dc.typePre-Publicación
dc.typehttp://purl.org/coar/resource_type/c_816b
dc.typeText
dc.typeinfo:eu-repo/semantics/preprint
dc.typeinfo:eu-repo/semantics/draft
dc.typehttp://purl.org/redcol/resource_type/ARTOTR
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.typehttp://purl.org/coar/version/c_ab4af688f83e57aa


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